Towards a Deep Leaning-based Approach for Hadith Classification


  •   Moath Mustafa Ahmad Najeeb


The exploration of Hadith sciences gains significant consideration over the most recent couple of years. Hadith is mostly the sayings of Prophet Mohammad. The Holy Quran represents the first origin of law in Islam then Hadith takes the second role. Many research efforts manage Hadith with respect to the “Isnad” and “Matn”; which are the main two pieces of Hadith. In this paper, we examine the chance of utilizing Deep Learning to process Isnad of Hadiths. Consequently, a definitive objective of our framework is to help in the systematic classification of Hadiths and differentiate among the correct (“Sahih”) Hadiths and the not accurate (“Da'ief”) Hadiths.

Keywords: Deep Learning, Classification, Hadith, Arabic Language Processing, Isnad


A. Abdelkader, D. Souilem Boumiza and R. Braham, “A categorization algorithm for the Arabic language,” International Conference on Communication, Computer and Power (ICCCP'09), Muscat, February 2009.

NLP4Arabic. Available online: (Accessed on 12 Jul 2020).

A. Farghaly, K. Shaalan, “Arabic natural language processing: challenges and solutions”. ACM Trans. Asian Lang. Inform. Process. 8, 4, Article 14, 22 pages, December 2009.

K. Shaalan, “Rule-based approach in Arabic natural language processing,” International Journal on Information and Communication Technologies, Vol. 3, No. 3, June 2010.

M. Al-Hajjaj, “Sahih Muslim” [Muslim probe] (in Arabic), Dar Ibn Al-Jawzi Publication and Distributors, Egypt, 2009.

M. Najeeb, A. Abdelkader, and M. Al-Zghoul, “Arabic natural language processing laboratory serving Islamic sciences,” Int. J. Adv. Comput. Sci. Applic. 2014, vol. 5, no. 3, pp. 114-117.

M. Najeeb, “A Novel Hadith Processing Approach Based on Genetic Algorithms,” IEEE Access, vol. 8, 2020, pp. 20233-20244.

M. Najeeb, “XML Database for Hadith and Narrators,” American Journal of Applied Sciences. 2016, vol. 13, no. 1, pp. 55-63.

M. Najeeb, A. Abdelkader, M. Al-Zghoul, and A. Osman, “A lexicon for hadith science based on a corpus,” Int. J. Comput. Sci. Inform. Technol. 2015, vol. 6, no. 2 pp. 1336-1340.

M. Saloot, N. Idris, R. Mahmud, S. Ja’afar, D. Thorleuchter, and A. Gani, “Hadith data mining and classification: a comparative analysis,” Artif Intell Rev. 2016, vol. 46, pp. 113 – 128.

F. Harrag, “Text mining approach for knowledge extraction in Sahîh Al-Bukhari,” Comput Hum Behav. 2014, vol. 30, pp. 558–566.

K. Aldhaln, A. Zeki, A. Zeki, and H. Alreshidi, “Improving knowledge extraction of Hadith classifier using decision tree algorithm,” Int. conf. on information retrieval & knowledge management, 2012, Malaysia, pp. 148–152.

M. Alhawarat, “A domain-based approach to extract Arabic person names using n-grams and simple rules,” Asian Journal of Information Technology. 2015, vol. 14, no. 8, pp. 287–293.

A. Mahmood, H. Khan, F. Alarfaj, M. Ramzan, and M. Ilyas, “A multilingual datasets repository of the hadith content,” International Journal of Advanced Computer Science and Applications. 2018, vol. 9, no. 2, pp. 165–172.

Shamela.موقع المكتبة الشاملة على الشبكة العنكبوتية , Available online: (Accessed on 16 Jul 2020).

Dorar. موقع الدرر السنية على الشبكة العنكبوتية, Available online: (Accessed on 16 Jul 2020).

Islamweb. موقع إسلام ويب على الشبكة العنكبوتية, Available online: (Accessed on 16 Jul 2020).

Sonnaonline. الجامع للحديث النبوي, Available online: (Accessed on 20 Jul 2020).

Sunnah. Sayings and teachings of Prophet Muhammad, Available online: (Accessed on 20 Jul 2020).

Sunnah Alifta. جامع خادم الحرمين الشريفين للسنة النبوية, Available online: (Accessed on 20 Jul 2020).

A. Azmi and N. Bin Badia, "iTree - Automating the construction of the narration tree of Hadiths (Prophetic Traditions)," Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE-2010), Beijing, China, 2010, pp. 1-7.

F. Harrag, A. Hamdi-Cherif and E. El-Qawasmeh, "Vector space model for Arabic information retrieval — application to “Hadith” indexing," 2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), Ostrava, Czech Republic, 2008, pp. 107-112.

K. Aldhlan, A. Zeki, A. Zeki, “Data mining and Islamic Knowledge Extraction: Hadith as A Knowledge Resource”, Proceeding 3rd International Conference on ICT4M, 2010.

H. Alrazo, “Al-'Utur al-ma'lumatiah le tadawel al-ma'refah aleslamiah fi zaman al-a'wlamh: Information frame works to deal with Islamic Knowledge in globalization era”. Journal of Islamic knowledge 4, pp. 33-34, 2003.

H. Alrazo. “Data mining application on the Islamic knowledge resource”, from Alukah: (Accessed on 17 Aug 2020).

M. Ghazizadeh, M. Zahedi, M. Kahani, and B. Bidgoli, “Fuzzy Expert system in determining Hadith validity”, advances in computer and information sciences and engineering, PP.354-359, 2008.

M. Hyder and S. Ghazanfer, “Towards a database Oriented Hadith Research Using Relational, Algorithmic and Data-warehousing Techniques”, The Islamic Culture, Quarterly Journal of Shaikh Zayed Islamic Center for Islamic and Arabic Studies, Vol. 19, University of Karachi, 2008.

F. Harrag, E. El-Qawasmeh and A. Al-Salman, “Extracting Named Entities from Prophetic Narration Texts (Hadith)”, ICSECS 2011, Part II, CCIS 180, pp. 289–297, 2011.

K. Bilal and S. Mohsin, “Muhadith: A Cloud based Distributed Expert System for Classification of Ahadith”, IEEE 10th International Conference on Frontiers of Information Technology, pp. 73-78, 2012.

W. Sari, M. Arif Bijaksana, and A. Huda, “Indexing Name in Hadith Translation Using Hidden Markov Model (HMM),” 7th International Conference on Information and Communication Technology (ICoICT), 2019, Kuala Lumpur, Malaysia, pp. 1-5, 2019.

I. Bounhas, “On the Usage of a Classical Arabic Corpus as a Language Resource: Related Research and Key Challenges,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 2019, vol. 18, no. 3, Art. no. 23.

A. Azmi, A. Al-Qabbany, and A. Hussain, “Computational and natural language processing-based studies of hadith literature: a survey,” Artificial Intelligence Review. 2019, vol. 52, no. 2, pp. 1369-1414.

S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, J. Gao Deep learning-based text classification: a comprehensive review, arXiv Preprint arXiv:2004.03705 (2020).

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

Microscope uses artificial intelligence to find cancer cells more efficiently. Available online: (Accessed on 18 Nov 2020).

Deep Image Prior. Available online: (Accessed on 25 Nov 2020).

How deep learning is changing the game for both advertisers and consumers. Available online: (Accessed on 25 Nov 2020).

Machine Learning for Translation: What’s the State of the Language Art? Available online: (Accessed on 29 Nov 2020).

Why deep-learning AIs are so easy to fool. Available online: (Accessed on 29 Nov 2020).

R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, pp. 1631–1642.

Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” in Proc. ICML, 2014, pp. 1188–1196, 2014.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. ICLR, 2013.

P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv preprint arXiv:1605.05101, 2016.

Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. ICLR, 2015.

Z. Yang, D. Yang, C. Dyer, X. He, A. J. Smola, and E. H. Hovy, “Hierarchical attention networks for document classification,” in Proc. NAACL, 2016, pp. 1480–1489, 2016.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems, 2017, pp. 5998–6008.

Y. Li, R. Jin, and Y. Luo, “Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (seggcrns),” JAMIA, vol. 26, no. 3, pp. 262–268, 2019.

D. Marcheggiani and I. Titov, “Encoding sentences with graph convolutional networks for semantic role labeling,” in Proc. EMNLP, 2017, pp. 1506–1515, 2017.

J. Bastings, I. Titov,W. Aziz, D. Marcheggiani, and K. Sima’an, “Graph convolutional encoders for syntax-aware neural machine translation,” in Proc. EMNLP, 2017, pp. 1957–1967, 2017.


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How to Cite
Najeeb, M.M.A. 2021. Towards a Deep Leaning-based Approach for Hadith Classification. European Journal of Engineering and Technology Research. 6, 3 (Mar. 2021), 9–15. DOI: